BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning

• URL: http://arxiv.org/abs/2211.14744v1
• Date: Sun, 27 Nov 2022 06:36:35 GMT
• Title: BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning
• Authors: Chi Zhang, Yuanyuan Shi, Yize Chen
• Abstract summary: "BEAR" is a physics-principled Building Environment for Control And Reinforcement Learning. It allows researchers to benchmark both model-based and model-free controllers using a broad collection of standard building models in Python without co-simulation using external building simulators. We demonstrate the compatibility and performance of BEAR with different controllers, including both model predictive control (MPC) and several state-of-the-art RL methods with two case studies.
• Score: 9.66911049633598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dynamical model and energy management task simulation and evaluation platform has arguably slowed the progress in developing advanced and dedicated reinforcement learning (RL) and control algorithms for building operation tasks. Here we propose "BEAR", a physics-principled Building Environment for Control And Reinforcement Learning. The platform allows researchers to benchmark both model-based and model-free controllers using a broad collection of standard building models in Python without co-simulation using external building simulators. In this paper, we discuss the design of this platform and compare it with other existing building simulation frameworks. We demonstrate the compatibility and performance of BEAR with different controllers, including both model predictive control (MPC) and several state-of-the-art RL methods with two case studies.

Related papers

• Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving [52.808273563372126]
  This paper proposes a novel hierarchical BEV perception paradigm, aiming to provide a library of fundamental perception modules and user-friendly graphical interface. We conduct the Pretrain-Finetune strategy to effectively utilize large scale public datasets and streamline development processes. We also present a Multi-Module Learning (MML) approach, enhancing performance through synergistic and iterative training of multiple models.
  arXiv  Detail & Related papers  (2024-07-17T11:17:20Z)
• Benchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST) [40.17692290400862]
  We present a data-driven modeling and control framework for physics-based building emulators. Our approach consists of: (a) Offline training of differentiable surrogate models that accelerate model evaluations, provide cost-effective gradients, and maintain good predictive accuracy for the receding horizon in Model Predictive Control (MPC) We extensively evaluate the modeling and control performance using multiple surrogate models and optimization frameworks across various test cases available in the Building Optimization Testing Framework (BOPTEST)
  arXiv  Detail & Related papers  (2023-01-31T06:55:19Z)
• Exploring Deep Reinforcement Learning for Holistic Smart Building Control [3.463438487417909]
  We develop a system called OCTOPUS that uses a data-driven approach to find the optimal control sequences of all building's subsystems. OCTOPUS can achieve 14.26% and 8.1% energy savings compared with the state-of-the-art rule-based method in a LEED Gold Certified building.
  arXiv  Detail & Related papers  (2023-01-27T03:03:21Z)
• A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation [59.56144813928478]
  In current control strategies and optimization algorithms, most of them rely on receiving information from real-time feedback. We propose an engineer-friendly control strategy framework that embeds dynamic prior knowledge from building system characteristics simultaneously for system control. We tested it in a case for heating system control with typical control strategies, which shows our framework owns a further energy-saving potential of 15%.
  arXiv  Detail & Related papers  (2023-01-23T09:07:07Z)
• GEM: Group Enhanced Model for Learning Dynamical Control Systems [78.56159072162103]
  We build effective dynamical models that are amenable to sample-based learning. We show that learning the dynamics on a Lie algebra vector space is more effective than learning a direct state transition model. This work sheds light on a connection between learning of dynamics and Lie group properties, which opens doors for new research directions.
  arXiv  Detail & Related papers  (2021-04-07T01:08:18Z)
• Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control [0.0]
  We propose an open-source environment for multiple quadcopters based on the Bullet physics engine. Its multi-agent and vision based reinforcement learning interfaces, as well as the support of realistic collisions and aerodynamic effects, make it, to the best of our knowledge, a first of its kind.
  arXiv  Detail & Related papers  (2021-03-03T02:47:59Z)
• Sim-Env: Decoupling OpenAI Gym Environments from Simulation Models [0.0]
  Reinforcement learning (RL) is one of the most active fields of AI research. Development methodology still lags behind, with a severe lack of standard APIs to foster the development of RL applications. We present a workflow and tools for the decoupled development and maintenance of multi-purpose agent-based models and derived single-purpose reinforcement learning environments.
  arXiv  Detail & Related papers  (2021-02-19T09:25:21Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)
• A Sequential Modelling Approach for Indoor Temperature Prediction and Heating Control in Smart Buildings [4.759925918369102]
  This paper proposes a learning-based framework for sequentially applying the data-driven statistical methods to predict indoor temperature. Experiments demonstrate the effectiveness of the modelling approach and control algorithm, and reveal the promising potential of the mixed data-driven approach in smart building applications.
  arXiv  Detail & Related papers  (2020-09-21T13:20:27Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.